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Evaluation of Fear Using Nonintrusive Measurement of Multimodal Sensors.

Choi JS, Bang JW, Heo H, Park KR - Sensors (Basel) (2015)

Bottom Line: Further, the latter causes inconvenience to the user due to the sensors attached to the body.Among various emotions, the accurate evaluation of fear is crucial in many applications, such as criminal psychology, intelligent surveillance systems and the objective evaluation of horror movies.Therefore, we propose a new method for evaluating fear based on nonintrusive measurements obtained using multiple sensors.

View Article: PubMed Central - PubMed

Affiliation: Division of Electronics and Electrical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Korea. jjongssuk@dgu.edu.

ABSTRACT
Most previous research into emotion recognition used either a single modality or multiple modalities of physiological signal. However, the former method allows for limited enhancement of accuracy, and the latter has the disadvantages that its performance can be affected by head or body movements. Further, the latter causes inconvenience to the user due to the sensors attached to the body. Among various emotions, the accurate evaluation of fear is crucial in many applications, such as criminal psychology, intelligent surveillance systems and the objective evaluation of horror movies. Therefore, we propose a new method for evaluating fear based on nonintrusive measurements obtained using multiple sensors. Experimental results based on the t-test, the effect size and the sum of all of the correlation values with other modalities showed that facial temperature and subjective evaluation are more reliable than electroencephalogram (EEG) and eye blinking rate for the evaluation of fear.

No MeSH data available.


Related in: MedlinePlus

(a) Detected face and facial feature regions in the visible-light image; (b) mapped face and facial feature regions in the thermal image after geometric transformation.
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sensors-15-17507-f006: (a) Detected face and facial feature regions in the visible-light image; (b) mapped face and facial feature regions in the thermal image after geometric transformation.

Mentions: Figure 6 shows the detected facial feature regions, such as the face, eyes, nose and nostrils in visible-light and thermal images. An adaptive boosting (AdaBoost) algorithm is used to detect the face region [28]. Then, the search areas of eyes and nose are defined within the detected face region. Within the search areas, the eyes and nose regions are located using the AdaBoost algorithm. Our method performs image binarization in the detected nose region to accurately detect both nostrils. Based on the detected nostrils, the center of the nose is detected. As explained in Section 2.1, the light is turned off while the user watches the movie to maximize the user’s fear. Therefore, additional NIR illuminators are used, as shown in Figure 2; the images from the visible-light camera are obtained under NIR illumination, and the NIR cutting filter inside the web-camera is replaced by an NIR passing filter.


Evaluation of Fear Using Nonintrusive Measurement of Multimodal Sensors.

Choi JS, Bang JW, Heo H, Park KR - Sensors (Basel) (2015)

(a) Detected face and facial feature regions in the visible-light image; (b) mapped face and facial feature regions in the thermal image after geometric transformation.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541947&req=5

sensors-15-17507-f006: (a) Detected face and facial feature regions in the visible-light image; (b) mapped face and facial feature regions in the thermal image after geometric transformation.
Mentions: Figure 6 shows the detected facial feature regions, such as the face, eyes, nose and nostrils in visible-light and thermal images. An adaptive boosting (AdaBoost) algorithm is used to detect the face region [28]. Then, the search areas of eyes and nose are defined within the detected face region. Within the search areas, the eyes and nose regions are located using the AdaBoost algorithm. Our method performs image binarization in the detected nose region to accurately detect both nostrils. Based on the detected nostrils, the center of the nose is detected. As explained in Section 2.1, the light is turned off while the user watches the movie to maximize the user’s fear. Therefore, additional NIR illuminators are used, as shown in Figure 2; the images from the visible-light camera are obtained under NIR illumination, and the NIR cutting filter inside the web-camera is replaced by an NIR passing filter.

Bottom Line: Further, the latter causes inconvenience to the user due to the sensors attached to the body.Among various emotions, the accurate evaluation of fear is crucial in many applications, such as criminal psychology, intelligent surveillance systems and the objective evaluation of horror movies.Therefore, we propose a new method for evaluating fear based on nonintrusive measurements obtained using multiple sensors.

View Article: PubMed Central - PubMed

Affiliation: Division of Electronics and Electrical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Korea. jjongssuk@dgu.edu.

ABSTRACT
Most previous research into emotion recognition used either a single modality or multiple modalities of physiological signal. However, the former method allows for limited enhancement of accuracy, and the latter has the disadvantages that its performance can be affected by head or body movements. Further, the latter causes inconvenience to the user due to the sensors attached to the body. Among various emotions, the accurate evaluation of fear is crucial in many applications, such as criminal psychology, intelligent surveillance systems and the objective evaluation of horror movies. Therefore, we propose a new method for evaluating fear based on nonintrusive measurements obtained using multiple sensors. Experimental results based on the t-test, the effect size and the sum of all of the correlation values with other modalities showed that facial temperature and subjective evaluation are more reliable than electroencephalogram (EEG) and eye blinking rate for the evaluation of fear.

No MeSH data available.


Related in: MedlinePlus